A Simultaneous Multiscale Data Assimilation Using Scale-Dependent Localization in GSI-Based Hybrid 4DEnVar for NCEP FV3-Based GFS

Abstract

A scale-dependent localization (SDL) method was formulated and implemented in the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (4DEnVar) system for NCEP FV3-based Global Forecast System (GFS). SDL applies different localization to different scales of ensemble covariances, while performing a single-step simultaneous assimilation of all available observations. Two SDL variants with (SDL-Cross) and without (SDL-NoCross) considering cross-wave-band covariances were examined. The performance of two- and three-wave-band SDL experiments (W2 and W3, respectively) was evaluated through 1-month cycled data assimilation experiments. SDL improves global forecasts to 5 days over scale-invariant localization including the operationally tuned level-dependent scale-invariant localization (W1-Ope). The W3 SDL-Cross experiment shows more accurate tropical storm–track forecasts at shorter lead times than W1-Ope. Compared to the W2 SDL experiments, the W3 SDL counterparts applying tighter horizontal localization at medium-scale wave band generally show improved global forecasts below 100 hPa, but degraded global forecasts above 50 hPa. While the outperformance of the W3 SDL-NoCross experiment versus the W2 SDL-NoCross experiment below 100 hPa lasts for 5 days, that of the W3 SDL-Cross experiment versus the W2 SDL-Cross experiment lasts for 3 days. Due to local spatial averaging of ensemble covariances that may alleviate sampling error, the SDL-NoCross experiments show slightly better forecasts than the SDL-Cross experiments at shorter lead times. However, the SDL-Cross experiments outperform the SDL-NoCross experiments at longer lead times, likely from retention of more heterogeneity of ensemble covariances and resultant analyses with improved balance. Relative performance of tropical storm–track forecasts in the W2 and W3 SDL experiments are generally consistent with that of global forecasts.

Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results

“…This result implies that increments in RADAR_H15_V1.1 were featured by larger contributions of all scales compared to REF1. These reduced contributions of all scales in REF1 are speculated to have been caused by the dampened correlation at small scales and then the weakened cross-correlations between small and large scales when applying tighter localization radii for radar DA, consistent with [76,77]. For the wind increments, similar tendencies for the spectral differences were displayed in the two experiments (not shown).…”

“…To determine an optimal set of parameters, additional experiments are required to test a wider range of parameters. Additionally, as suggested by the results in Section 4.1, a scale-dependent localization of ensemble covariances [75,76] is ideally preferred to estimate all scales by assimilating all available observations simultaneously. Furthermore, as the quality of ensemble-based BECs may be degraded by a zero or small variance of hydrometeor mixing ratios for radar DA, an additive noise inflation [22,23,36,49] and/or properly constructed convective-scale static BECs can efficiently address the under-dispersive ensemble spread in storm scales [82].…”

“…However, a wider radius is needed for properly analyzing storms in northern Minnesota as in CONV_H500_V1.1. The adaptive localization functions [73,74] or the scale-dependent localization method [75,76] may be more appropriate to address these multiscale DA problems in the future study.…”

Improving Global Weather Prediction in GFDL SHiELD Through an Upgraded GFDL Cloud Microphysics Scheme

“…(2020) have described key features of the GFDL MP, including thermodynamic consistency with the dynamical core, fast and stable sedimentation processes, and tight coupling between dynamics and physics. The GFDL MP is used in the operational GFS version 15 and 16 (Huang et al., 2021; Patel et al., 2021; Tong et al., 2020) and several other weather and climate models, including GFDL radiative‐convective equilibrium (RCE) simulations within a limited domain (Jeevanjee, 2017), the GFDL High‐resolution Atmosphere Model (HiRAM; Chen and Lin (2011), Chen and Lin (2013), Gao et al. (2017, 2019), Harris et al.…”

“…Zhou et al (2019) and have described key features of the GFDL MP, including thermodynamic consistency with the dynamical core, fast and stable sedimentation processes, and tight coupling between dynamics and physics. The GFDL MP is used in the operational GFS version 15 and 16 (Huang et al, 2021;Patel et al, 2021;Tong et al, 2020) and several other weather and climate models, including GFDL radiative-convective equilibrium (RCE) simulations within a limited domain (Jeevanjee, 2017), the GFDL High-resolution Atmosphere Model (HiRAM; Chen and Lin (2011), Chen and Lin (2013), Gao et al (2017Gao et al ( , 2019, Harris et al (2016)), the GFDL System for High-resolution prediction on Earth-to-Local Domains (SHiELD; ), the National Oceanic and Atmospheric Administration's Hurricane Analysis and Forecast System (HAFS; Dong et al (2020); Hazelton et al (2021)), the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System Model (He et al, 2019;Li et al, 2019;Zhou et al, 2015), and the National Aeronautics and Space Administration Goddard Earth Observing System (GEOS) version 5 (Arnold et al, 2020).…”